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Freelance Python Data Analysis Jobs in Tucson, AZ

University of Arizona

Scientific Analyst II

Tucson, AZ · On-site

Data Analysis and Machine Learning Pipeline Development: * Under moderate guidance collaborate in ... Collaborate in the develop and maintenance of reproducible data pipelines using Python, R, and high ...

AirLife

Data Analyst - Commercial

Tucson, AZ · On-site

Experience with SQL, Python, R or other programming tools. * Experience with project management ... Project manage key data analytics initiatives by leading requirements gathering, coordinating ...

Lunewave, Inc.

Sensor Validation Engineer

Tucson, AZ · On-site

Experience in data analysis, including statistics * Experience using MATLAB, Python or similar for data analysis * Experience presenting complicated topics to high level stakeholders * Validate ...

Lunewave, Inc.

Sensor Validation Engineer

Tucson, AZ · On-site

Experience in data analysis, including statistics * Experience using MATLAB, Python or similar for data analysis * Experience presenting complicated topics to high level stakeholders * Validate ...

Varsity Tutors

Data Science Tutor

Tucson, AZ · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Raytheon

Factory Data Analytics Engineer

Tucson, AZ

$108K - $130K/yr

Create and maintain data visualization and statistical analysis tools that increase understanding ... Python based data inquiry (Pandas, NumPy, Scikit-learn, Matplotlib, etc) * Exposure to statistical ...

Danaher

Digital Analyst Internships

Tucson, AZ · On-site

$91K - $108K/yr

Familiarity with data analysis platforms and tools, comfortable extracting and interpreting complex ... Basic programming or scripting experience in Python, SQL, or JavaScript * Experience with Sitecore ...

Arete

Scientist

Tucson, AZ · On-site

Responsibilities include analyzing data, assessing algorithm performance, prototyping new ... Significant experience programming in Python, C/C++, MATLAB or similar languages * Experience ...

Arete Associates

Scientist

Tucson, AZ · On-site

$80K - $130K/yr

Responsibilities include analyzing data, assessing algorithm performance, prototyping new ... Significant experience programming in Python, C/C++, MATLAB or similar languages * Experience ...

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How much do freelance python data analysis jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for freelance python data analysis in Tucson, AZ is $55.43, according to ZipRecruiter salary data. Most workers in this role earn between $45.67 and $62.93 per hour, depending on experience, location, and employer.

What is freelance Python data analysis?

Freelance Python data analysis involves using the Python programming language to analyze and interpret data for clients on a project or contract basis. Freelancers in this field typically work with datasets to extract insights, visualize results, and help businesses make data-driven decisions. They often use libraries such as pandas, NumPy, and matplotlib, and may work across industries like finance, marketing, healthcare, and technology. Freelancers enjoy flexibility in choosing their projects and clients, and often work remotely.

What are the key skills and qualifications needed to thrive as a Freelance Python Data Analyst, and why are they important?

To thrive as a Freelance Python Data Analyst, you need strong analytical skills, proficiency in Python programming, and a solid understanding of statistics and data manipulation, often backed by a relevant degree or proven portfolio. Experience with tools such as Pandas, NumPy, Jupyter Notebooks, and data visualization libraries like Matplotlib or Seaborn is typically required. Excellent problem-solving abilities, communication skills, and the ability to manage projects independently distinguish top performers in this role. These skills enable analysts to deliver actionable insights, meet client expectations, and maintain a successful freelance business.

What are some common challenges freelance Python data analysts face when working with clients remotely?

Freelance Python data analysts often encounter challenges such as clarifying project requirements, managing client expectations about deliverables, and ensuring timely communication across different time zones. Working remotely can also mean troubleshooting data access or security issues, especially if clients have strict data privacy policies. Building trust through regular updates and transparent reporting is key to successful collaborations in this role.
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Scientific Analyst II

Scientific Analyst II

University of Arizona

Tucson, AZ • On-site

Other

Re-posted 27 days ago

University Of Arizona rating

7.2

Company rating: 7.2 out of 10

Based on 67 frontline employees who took The Breakroom Quiz

345th of 555 rated colleges and universities

Job description

Data Analysis and Machine Learning Pipeline Development:

  • Under moderate guidance collaborate in the design, develop, and execution of machine learning and AI-driven analytical pipelines to analyze large-scale biomedical datasets from UK Biobank, All of Us, Insight, and electronic medical records.
  • Apply supervised and unsupervised machine learning algorithms (e.g., logistic regression, random forests, deep learning) to identify risk factors, biomarkers, and patterns associated with neurodegenerative diseases and the effects of menopausal hormone therapy (MHT) on brain health.
  • Collaborate on the development and validation of predictive models integrating genomic, clinical, lifestyle, and imaging data using general knowledge of principals, theories and concepts.

Drug Repurposing Research and Bioinformatics Analysis:

  • Collaborating in computational drug repurposing analyses to identify existing FDA-approved compounds with potential efficacy for AD, PD, MS, and ALS prevention and treatment. Integrate multi-omics data (genomics, transcriptomics, proteomics) with clinical outcomes data to prioritize drug candidates.
  • Collaborate with wet lab and clinical teams to support translational interpretation of findings.

Epidemiological and Clinical Data Management and Harmonization:

  • Access, curate, harmonize, and manage large population-based datasets including UK Biobank, All of Us, and institutional EMR data.
  • Ensure data quality, reproducibility, and compliance with data use agreements and IRB protocols.
  • Collaborate in the develop and maintenance of reproducible data pipelines using Python, R, and high performance computer.
  • Perform statistical analyses including survival analysis, longitudinal modeling, and causal inference.

Scientific Communication, Dissemination, and Collaboration:

  • Compare and contribute to peer-reviewed manuscripts, conference presentations, and grant applications reporting research findings on MHT, menopause, and neurodegenerative disease.
  • Present results to interdisciplinary research teams, departmental seminars, and external stakeholders.
  • Collaborate closely with Dr. Francesca Vitali, co-investigators, and consortium partners. Maintain thorough documentation of analytical methods to ensure transparency and reproducibility.
  • Participate in lab meetings, journal clubs, and professional development activities.

Research Infrastructure and Continuous Improvement:

  • Maintain and improve lab computational infrastructure, including code repositories (GitHub), analytical workflows, and documentation standards.
  • Evaluate and adopt emerging AI/ML tools and methodologies relevant to brain science research.
  • Assist in training junior lab members or graduate students on data science methods and tools as needed.
  • Stay current with literature in neurodegenerative disease, computational.

Knowledge, Skills and Abilities:

  • Strong theoretical and applied knowledge of machine learning, deep learning, and statistical modeling.
  • Strong data wrangling and preprocessing skills for large, heterogeneous datasets.
  • Expert-level programming skills in Python and/or R; proficiency with ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost).
  • Knowledge of drug repurposing methodologies or network pharmacology.
  • Knowledge and familiarity with electronic medical records data analysis.
  • Knowledge and proficiency with SQL and database management.
  • Ability to collaborate effectively within interdisciplinary teams spanning data science, neuroscience, clinical research, and epidemiology.
  • Ability to manage multiple concurrent projects and meet deadlines.
  • Ability to critically evaluate scientific literature and translate findings into research hypotheses and analytical strategies.
  • Ability to communicate complex analytical results clearly to both technical and non-technical audiences.

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